For data that conforms to a linear model, Ordinary Least Squares regression (OLS) provides a Best Linear Unbiased Estimator [1]}, as long as the underlying assumptions are not violated. The data used in this study exhibited both multicollinearity and heteroscedasticity. A delay in completion of any intermediate milestones will impact the prediction for the target milestone, and this cascading effect must be factored in the model. Hence, feature elimination was not an option. Ridge Regression (RR) was tested instead of OLS to mitigate the effects of multicollinearity. To further handle data exhibiting heteroscedasticity and provide predictive-bounds instead of point-estimates, Quantile Regression (QR) was adopted as the regression method of choice, in the proposed approach. Given the earlier discussion on data being noisy, median-based metrics were preferred (over mean-based metrics) both in the cost functions of modeling approaches and in performance metrics for their evaluation, to reduce the sensitivity of the proposed approach and its performance metrics to outlier data.
